To reduce the number of trainable parameters, model compression [1]} or binary network weights [2]} have been used. On the other hand, light-weight networks with shallow architectures have been applied to real-world scenarios that require real-time prediction and decision tasks [3]}, [4]}. Some have proposed to use network compression to remove redundancies for a pretrained model through pruning techniques [3]}. In [1]}, [2]} only a few bits are used to represent learned model weights instead of high-precision floating point numbers. These approaches do modify the network structure and often come at the cost of poor segmentation performance. Light-weight CNNs, on the other hand, are computationally cheap shallow networks with improved efficiency [8]}, [4]}. In such networks, to reduce the model size, convolutional factorization is often used. For example, depth-wise separable convolutions are employed by both ShuffleNet [8]} and MobileNets [4]}, where 1\(\times \) 1 point-wise and depth-wise convolutions are used instead of standard convolutions. In ERFNet [12]}, a 2D convolution is decomposed into two 1D-factorized convolutions. Despite their success, convolutional factorisation can undermine the ability of light-weight networks to learn discriminative local structures, leading to performance degradation [13]}. We have recently proposed the RC-Net [14]} and the more optimized T-Net [15]} light-weight networks, which use VGG16 as their base net. T-Net extracts low frequency features through the use of small pooling kernels. However, it performs poorly on datasets with high feature variation.
